Domain Adaptation from Generated Multi-Weather Images for Unsupervised Maritime Object Classification
This work addresses maritime safety and monitoring by improving classification in challenging weather and object distribution scenarios, representing an incremental advance through novel dataset construction and adaptation techniques.
The paper tackles maritime object classification by addressing long-tail data distributions in object categories and weather conditions through domain adaptation from generated multi-weather images to real-world data, resulting in significant accuracy improvements for rare categories and conditions.
The classification and recognition of maritime objects are crucial for enhancing maritime safety, monitoring, and intelligent sea environment prediction. However, existing unsupervised methods for maritime object classification often struggle with the long-tail data distributions in both object categories and weather conditions. In this paper, we construct a dataset named AIMO produced by large-scale generative models with diverse weather conditions and balanced object categories, and collect a dataset named RMO with real-world images where long-tail issue exists. We propose a novel domain adaptation approach that leverages AIMO (source domain) to address the problem of limited labeled data, unbalanced distribution and domain shift in RMO (target domain), enhance the generalization of source features with the Vision-Language Models such as CLIP, and propose a difficulty score for curriculum learning to optimize training process. Experimental results shows that the proposed method significantly improves the classification accuracy, particularly for samples within rare object categories and weather conditions. Datasets and codes will be publicly available at https://github.com/honoria0204/AIMO.